1. Field of the Invention
The present invention relates to a system and a method for determining the optimum order in article and volume, and supporting the management of top sellers and shelf warmers.
2. Description of the Related Art
Conventionally, for example, in convenience stores, etc., the articles(merchandise) management in each store has been performed on top sellers and shelf warmers based on the sales results (actual sales volume) especially for daily delivered articles (lunch boxes, rice balls, other fresh articles, etc. which are fundamentally ordered and delivered every day).
Normally, orders have been placed on the daily delivered articles by a person in charge. That is, the variations of items(merchandise) are determined by the person in charge. The estimate of volume of each article is determined based on the sales results of the past, for example, the day before, the week before, etc. The volume adjustment has been made based on the clearance determination by trial and error by a person in charge. The influence of the volume in the previous delivery on the current delivery (due to the overlapping sales periods (from delivery time to sell-by time)) has also been taken into account by the person in charge.
The management of top sellers and shelf warmers and the estimate of volume are performed based on the sales results of the past. For example, if the order volume of an article is 5 and the sales result of the article is 5, then the article could have been sold more if the order volume had been larger (that is, there can be a loss of sales opportunities). On the other hand, if the order volume is too large, there can be unsold articles to be wastefully discarded (that is, there can be a loss due to the discarding process).
However, the sales trend of each article depends on each store, and the sales trends of various articles are different from one another in the same store day by day, thereby making it difficult for a person in charge to appropriately determine the order volume of each article through experience or by trial and error. Furthermore, it takes a long time to determine the optimum order volume for each article.
To solve the problem of the variations in optimum order volume (to solve the problem of uncertain variations), for example, there is a method for optimally estimating the sales volume of each article by analyzing the factors of variations, reducing the variations, and quantifying the influence of the variations using the regression analysis, the tree analysis, etc.
However, in the approach of estimating the sales volume as described above, it is very difficult for the following reasons to generate an estimated model capable of outputting a high-precision estimate.                A large volume of data cannot be obtained on the same condition (The conditions are different between today and yesterday, between stores, etc., thereby hardening the settings on the same condition.)        There are no sufficient probable factors for the variations (for example, detailed attributes of lunch boxes, etc.).        